Smoking is the leading preventable cause of death and disability in the U.S., and a major cause of health disparities. African American (AA) smokers have higher overall rates of tobacco-related morbidity and mortality and lower smoking cessation rates than do white smokers. Unfortunately, the search for effective policies and interventions to reduce smoking among AAs is severely hampered by the paucity of research on the mechanisms underlying smoking cessation in this population. This longitudinal cohort study will examine the influence of demographics and social history, biobehavioral/psychosocial predispositions, contextual and environmental influences, and acute momentary precipitants on smoking lapse and abstinence among 300 AA smokers attempting to quit. This study is guided by an overarching conceptual framework derived from models of the social determinants of health, social cognitive theories, and prior empirical findings. Participants will be assessed usin real-time, field-based, state of the science methodologies consisting of ecological momentary assessment (EMA), AutoSense, and geographic positioning system (GPS). AutoSense tracks behavioral and physiologic data in real-time and can objectively detect when an individual smokes or experiences negative affect/stress. GPS permits real-time spatial mapping of location patterns, which can be paired with EMA and Autosense data, and with relevant environmental exposures/characteristics (e.g., tobacco outlet exposure; area-level poverty) using geographic information system data. Principal outcomes of interest are lapse ascertained in real time through AutoSense, and early and long-term abstinence from smoking. This research would be the first to ever combine objective and dynamic indices of smoking lapse, negative affect/stress, and key environmental influences in the study of smoking cessation. The comprehensive, multi-method approach is a major advance for the field as it eliminates problems related to an exclusive reliance on self-report for key outcomes and predictors. In addition, this is one off the first studies to include empirically based machine learning approaches to fully mine the voluminous body of data yielded by real time assessment approaches, and to include the framework of dynamic prediction models, a novel statistical approach. The results will inform the tailoring of policies and interventions targeted at reducing the profound smoking-related disparities experienced by AAs.